Super Resolution of Multispectral Images using ℓ1 Image Models and Interband Correlations

Miguel Vega, Javier Mateos*, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


In this paper we propose a novel super-resolution based algorithm for the pansharpening of multispectral images. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images; that is, it imposes smoothness within each band by means of the energy associated with the ℓ1 norm of vertical and horizontal first order differences of image pixel values and also takes into account the correlation among the bands of the multispectral image. The observation process is modeled using the sensor characteristics of both panchromatic and multispectral images. The method is tested on real and synthetic images, compared with other pansharpening methods, and the quality of the results assessed both qualitatively and quantitatively.

Original languageEnglish (US)
Pages (from-to)509-523
Number of pages15
JournalJournal of Signal Processing Systems
Issue number3
StatePublished - Dec 2011


  • Bayesian approach
  • Interband correlations
  • Multispectral images
  • Pansharpening
  • Super-resolution
  • Variational methods
  • ℓ1 image models

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modeling and Simulation
  • Hardware and Architecture


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